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Home > Data > Data Analysis > Data Analysis with Pandas and Python
Data Analysis with Pandas and Python
Data Analysis with Pandas and Python

Data Analysis with Pandas and Python: Master Pandas: Easy Data Analysis in Python for Beginners!

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Profile Icon Boris Paskhaver
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$9.99 $89.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (4 Ratings)
Video Feb 2024 19hrs 26mins 1st Edition
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Profile Icon Boris Paskhaver
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$9.99 $89.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (4 Ratings)
Video Feb 2024 19hrs 26mins 1st Edition
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Key benefits

  • Detailed step-by-step guidance on the installation and setup of key data analysis tools.
  • Comprehensive coverage of Pandas functionalities, from basics to advanced techniques.
  • Focus on real-world applications, enhancing your ability to analyze complex datasets.

Description

This course begins with the essentials, introducing you to Anaconda and Jupyter Lab setup for Python and Pandas. You'll gain foundational knowledge in Python before diving into Pandas for data analysis. The focus then shifts to Series and DataFrame structures, providing you with the skills to manage and manipulate data effectively. Further, the course covers handling dates and times, and performing various file input and output operations, essential for real-world data analysis tasks. Advanced sections delve into data visualization using Matplotlib, enabling you to create impactful charts and graphs. You'll also explore advanced Pandas options and settings, enhancing your data manipulation capabilities. By the end of this course, you'll have a comprehensive understanding of data analysis techniques. You'll be equipped to handle complex datasets, perform detailed analysis, and present data visually, opening doors to advanced data analysis and manipulation in professional settings. Resources: https://github.com/PacktPublishing/Data-Analysis-with-Pandas-and-Python

Who is this book for?

Ideal for data analysts, aspiring data scientists, and professionals keen on mastering data manipulation and analysis. This course is a perfect fit for those with basic Python knowledge looking to delve deep into data analytics using Pandas. Whether you're aiming to enhance your skillset for professional growth or apply data analysis techniques in your current role, this course offers a comprehensive learning path from Python basics to advanced data handling and visualization techniques in Pandas.

What you will learn

  • Understand and utilize Python's basic and advanced data types.
  • Master Series and DataFrame operations in Pandas.
  • Handle complex data types like dates and times.
  • Perform input and output operations with different file types.
  • Create and customize various types of data visualizations.
  • Optimize data analysis with advanced Pandas settings and functions.

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Last updated date : Feb 12, 2024
Publication date : Oct 18, 2017
Length: 19hrs 26mins
Edition : 1st
Language : English
ISBN-13 : 9781788622394
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Pandas

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Product Details

Last updated date : Feb 12, 2024
Publication date : Oct 18, 2017
Length: 19hrs 26mins
Edition : 1st
Language : English
ISBN-13 : 9781788622394
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Pandas

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Table of Contents

15 Chapters
Installation and Setup Chevron down icon Chevron up icon
Introduction to the Course
macOS - Download and Install the Anaconda Distribution
Windows - Download and Install the Anaconda Distribution
Use Anaconda Navigator to Create a New Environment
Unpack Course Materials + The Startdown and Shutdown Process
Intro to the Jupyter Lab Interface
Code Cell Execution
Import Libraries into Jupyter Lab
Python Crash Course Chevron down icon Chevron up icon
Comments
Basic Data Types
Operators
Variables
Built-in Functions
Custom Functions
String Methods
Lists
Index Positions and Slicing
Dictionaries
Classes
Navigating Libraries using Jupyter Lab
Series Chevron down icon Chevron up icon
Create a Series Object from a List
Create a Series Object from a Dictionary
Intro to Series Methods
Intro to Attributes
Parameters and Arguments
Import Series with the pd.read_csv Function
The head and tail Methods
Passing Series to Python Built-In Functions
Check for Inclusion with Python's in Keyword
The sort_values Method
The sort_index Method
Extract Series Values by Index Position
Extract Series Values by Index Label
The get Method
Overwrite a Series Value
The copy Method
Math Methods on Series Objects
Broadcasting
The value_counts Method
The apply Method
The map Method
DataFrames I: Introduction Chevron down icon Chevron up icon
Methods and Attributes between Series and DataFrames
Differences between Shared Methods
Select One Column from a DataFrame
Select Multiple Columns from a DataFrame
Add New Column to DataFrame
A Review of the value_counts Method
Drop DataFrame Rows with Missing Values
Fill in Missing Values with the fillna Method
The astype Method I
The astype Method II
Sort a DataFrame with the sort_values Method I
Sort a DataFrame with the sort_values Method II
Sort DataFrame with the sort_index Method
Rank Series Values with the rank Method
DataFrames II: Filtering Data Chevron down icon Chevron up icon
This Module's Dataset + Memory Optimization
Filter a DataFrame Based on a Condition
Filter with More than One Condition (AND - &)
Filter with More than One Condition (OR - |)
The isin Method
The isnull and notnull Methods
The between Method
The duplicated Method
The drop_duplicates Method
The unique and nunique Methods
DataFrames III: Data Extraction Chevron down icon Chevron up icon
This Module's Dataset
The set_index and reset_index Methods
Retrieve Rows by Index Position with iloc Accessor
Retrieve Rows by Index Label with loc Accessor
Second Arguments to loc and iloc Accessors
Overwrite Value in a DataFrame
Overwrite Multiple Values in a DataFrame
Rename Index Labels or Columns in a DataFrame
Delete Rows or Columns from a DataFrame
Create Random Sample with the sample Method
The nsmallest and nlargest Methods
Filtering with the where Method
The apply Method with DataFrames
Working with Text Data Chevron down icon Chevron up icon
This Module's Dataset
Common String Methods
Filtering with String Methods
String Methods on Index and Columns
The split Method
More Practice with Splits
The expand and n Parameters of the split Method
MultiIndex Chevron down icon Chevron up icon
Intro to the MultiIndex Module
Create a MultiIndex
Extract Index Level Values
Rename Index Lebels
The sort_index Method on a MultiIndex DataFrame
Extract Rows from a MultiIndex DataFrame
The transpose Method
The stack Method
The unstack Method
The pivot Method
The melt Method
The pivot_table Method
GroupBy Chevron down icon Chevron up icon
Intro to the GroupBy Module
The groupby Method
Retrieve A Group with the get_group Method
Methods on the GroupBy Object
Grouping by Multiple Columns
The agg Method
Iterating through Groups
Merging DataFrames Chevron down icon Chevron up icon
Intro to the Merging DataFrames Module
The pd.concat Function I
The pd.concat Function II
Left Joins
The left_on and right_on Parameters
Inner Joins I
Inner Joins II
Full-Outer Joins
Merging by Indexes with the left_index and right_index Parameters
The join Method
Working with Dates and Times Chevron down icon Chevron up icon
Intro to the Working with Dates and Times Module and Review of Python's datetime
The Timestamp and DatetimeIndex Objects
Create Range of Dates with pd.date_range Function
The dt Attribute
Selecting Rows from a DataFrame with DatetimeIndex
The DateOffset Object
Specialized Date Offsets
Timedeltas
Input and Output Chevron down icon Chevron up icon
Intro to the Input and Output Module
Export DataFrame to CSV File
Install openpyxl Library to Read and Write Excel Files
Import Excel File into pandas
Export Excel File from pandas
Visualization Chevron down icon Chevron up icon
Install matplotlib Library for Visualization
The plot Method
Modifying Plot Aesthetics with Templates
Bar Charts
Pie Charts
Options and Settings Chevron down icon Chevron up icon
Introduction to the Options and Settings Module
Changing Options with Attributes
Changing Options with Functions
The precision Option
Conclusion Chevron down icon Chevron up icon
Conclusion

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(4 Ratings)
5 star 75%
4 star 0%
3 star 25%
2 star 0%
1 star 0%
Bob May 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Learned a substantial amount about pandas by going through this course. It might not go through 100% of pandas, but still goes through more than enough for you to be able to pick up the rest yourself.
Subscriber review Packt
Evgeni Jul 11, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Incredibly useful and well-structured course. Thank you very much
Subscriber review Packt
ang soo pin Oct 11, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good pandas video, it shows all the steps of running pandas.
Feefo Verified review Feefo
THIRUVARUL SELVAN Feb 11, 2024
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
the code download video for downloading pandas.zip is missing and so is the link for downloading
Subscriber review Packt

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About the author

Profile icon Boris Paskhaver
Boris Paskhaver
Boris Paskhaver is a New York City-based software engineer, author, and Udemy instructor with a unique journey into tech. Graduating from NYU in 2013 with a degree in Business Economics and Marketing, he initially worked in various roles, including business analyst and data analyst, at several companies. His coding journey began accidentally while building projects with Python and JavaScript, leading him to passionately pursue programming. Without formal computer science education, Boris completed App Academy's full-stack web development bootcamp, diving headfirst into web development. As an instructor, Boris focuses on creating comprehensive, easy-to-understand courses, addressing the challenges he faced learning to code. He's driven by the intersection of technology and education, aiming to make programming accessible to all. Boris brings this passion to his teaching, helping others unlock the potential of coding.
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